Towards Robust AI: A Test Perspective
编号:6 访问权限:仅限参会人 更新:2021-08-14 15:07:53 浏览:270次 主旨报告

报告开始:2021年08月19日 11:15(Asia/Shanghai)

报告时间:45min

所在会场:[PS] Plenary Session(Openning, Keynotes 1-6) [PS1] Openning and Keynote 1/2/3

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摘要
While deep learning systems have outperformed humans in many domains, they may behave poorly when the test cases are not i.i.d. to the training samples, let alone carefully crafted adversarial examples. In this talk, we discuss problems and opportunities towards robust AI from a test perspective, including offline test solutions to ensure sufficient data-driven coverage and online test solutions to reject adversarial examples and out-of-distribution samples.
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报告人
Qiang Xu
Associate Professor The Chinese University of Hong Kong

Prof. Qiang Xu, The Chinese University of Hong Kong
Qiang Xu is an Associate Professor of Computer Science & Engineering at The Chinese University of Hong Kong. He received his B.E. and M.E. degrees in Telecommunication Engineering from Beijing University of Posts & Telecommunications, China, in 1997 and 2000, respectively. After working at a start-up integrated circuit design house for one and a half years, he continued his graduate study and received his Ph.D. degree in Electrical & Computer Engineering from McMaster University, Canada, in 2005, and then joined CUHK.
Dr. Xu leads the CUhk REliable computing laboratory (CURE Lab.) and CUHK MakerLab. His research interests include fault-tolerant computing, trusted computing and smart hardware design. He received the Best Paper Award in 2004 IEEE/ACM Design, Automation and Test in Europe (DATE). He has five other papers nominated for best paper award at prestigious conferences (e.g., DAC and ICCAD).
Dr. Xu is currently serving as an associate editor for IEEE Design&Test. He has also served as technical program committee members for a number of conferences on VLSI design and testing, including DAC, ITC, ICCAD, and DATE.

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重要日期
  • 会议日期

    08月18日

    2021

    08月20日

    2021

  • 05月10日 2021

    初稿截稿日期

  • 08月16日 2021

    提前注册日期

  • 08月19日 2021

    报告提交截止日期

  • 08月20日 2021

    注册截止日期

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